Performance-Preserving Event Log Sampling for Predictive Monitoring

TL;DR

This paper introduces an instance selection method for predictive process monitoring that accelerates training without sacrificing accuracy, addressing efficiency issues in current machine learning approaches.

Contribution

It proposes a novel instance sampling procedure that enhances training speed for predictive models while maintaining accuracy levels.

Findings

Significant increase in training speed for prediction models

Maintains reliable prediction accuracy

Applicable to next activity and remaining time predictions

Abstract

Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy.